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TL;DR:
Semantic search is a powerful way to search documents using a query. Learn how to use embeddings and similarity to build a semantic search model with this article. TL;DR: Semantic search enables searches using queries; learn how to use embeddings and similarity to build a semantic search model.
Disclaimer: This article uses Cohere for text generation.
Summary:
Semantic search is an effective way to search for documents that are related to a specific query. Instead of relying on exact keyword matches, semantic search uses natural language processing (NLP) to analyze the context of the query and match documents with similar topics. This makes search results more accurate and relevant to the user. Using embeddings and similarity is one way to build a semantic search model. Embeddings are mathematical representations of words and phrases that capture the context in which they are used. By comparing the embeddings of a query and documents in a corpus, semantic search can determine which documents are most relevant. Another useful tool for semantic search is similarity. This measures how closely related two documents are by looking at the number of common words or phrases they share. A higher similarity score indicates that two documents are more closely related, while a lower score indicates that they are more dissimilar. In summary, semantic search is a powerful tool for improving search accuracy. By using embeddings and similarity, you can create a semantic search model that can quickly and accurately determine which documents are most relevant to a query. With the right model in place, users can find the information they need faster and with more accuracy. Based on this article, it is clear that semantic search is a powerful tool for searching documents with a query. It is a more efficient way to search than traditional keyword-based search methods, as it takes into account the context of the query. With the use of embeddings and similarity, semantic search models can provide better search results than traditional methods.
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